AI Product Manager, Berlin
Define and drive the AI product roadmap, ensuring alignment with business objectives and user needs. Collaborate with cross-functional teams, including engineering, design, and marketing, to develop and launch AI-powered features. Conduct market research and analyze user feedback to identify opportunities for AI integration. Work closely with data scientists and machine learning engineers to optimize AI models for accuracy, performance, and user impact. Define key performance indicators (KPIs) to measure success and iterate based on data-driven insights. Stay up to date with AI trends, emerging technologies, and best practices to ensure products remain competitive. Ensure ethical AI usage and compliance with data privacy regulations.
Product Manager, Agent Builder
Define the agent building experience by shaping how users move from idea to implementation across journeys, chat, and workflows, creating clear abstractions for building complex agent behavior without unnecessary friction. Own the Agent Development Life Cycle by designing how users analyze conversations, make changes, test improvements, and release updates to ensure the loop between build, test, and learn is tight, fast, and intuitive. Build simulation and testing systems to define how agents are validated before deployment, creating tools for simulating real-world scenarios, identifying failures, and improving performance with confidence. Design reusable systems through packages by defining how integrations, skills, and agent behaviors are packaged, discovered, and reused, enabling users to compose sophisticated agents from modular building blocks that improve over time. Integrate AI copilots into the workflow by working closely on how Ghostwriter (build) and Explorer (analyze) fit into Agent Builder, deciding what is automated versus user-driven, and how AI augments each step of the workflow. Make agent quality measurable and actionable by defining evaluation frameworks and feedback systems so users can understand performance and systematically improve their agents.
Product Manager, Voice
Define the voice interaction model by shaping how agents handle real-time conversations including turn-taking, interruptions, latency, tone, and recovery from errors. Design what "human-quality" voice interaction means in practice. Build reliable real-time systems by working closely with engineering on streaming architectures, latency budgets, and failure handling to ensure agents respond quickly and consistently in production environments. Own the voice stack experience by partnering across ASR, TTS, LLMs, and telephony integrations to deliver a cohesive product, deciding on model choices, orchestration strategies, and component integration. Make voice measurable and improvable by defining evaluation metrics such as latency, interruption handling, resolution rate, and conversation quality, and build feedback loops for performance improvement. Translate real-world usage and edge cases like noisy environments, accents, and call flows into product direction by working closely with customers deploying voice agents in production.
Product Manager - Agents
The Product Manager is responsible for owning the vision, strategy, and roadmap for Axion's agentic systems, which are AI-driven workflows that autonomously detect issues, investigate root causes, and surface insights across customers' product data. They partner with Engineering and AI/ML teams to design and ship agentic pipelines that reason across heterogeneous data sources, define how agents orchestrate tasks, when to escalate to humans, and ensure agentic outputs are explainable and trustworthy for enterprise manufacturing teams. They collaborate with customers, Solutions, and Operations teams to understand workflows that agents need to augment or replace and translate these into clear product requirements. The Product Manager builds the short and long-term roadmap for agentic tooling by integrating customer feedback, emerging AI research, internal data, and strategic initiatives to continuously expand Axion's platform capabilities. They measure the impact of agentic features on key outcomes such as investigation speed, issue detection rates, and warranty cost reduction, iterating based on learning. They work with key internal and external stakeholders to guide prioritization and ensure focused, high-impact product development, while maintaining awareness of the big picture.
Senior Product Manager - Enterprise & User Management
The role involves owning Gong's internal AI operating model and driving internal digital transformation by bridging high-level business discovery with deep technical execution. Responsibilities include defining the internal AI roadmap in partnership with Security, Legal, and business leaders; operating the enterprise AI stack including large language models (LLMs), vector databases, and gateways; enforcing consistent patterns for tool calling, prompt versioning, state management, and error handling; managing the full model lifecycle from evaluation to deprecation. The role also requires proactively interviewing various teams to identify manual workflows for automation via agentic AI, independently building and deploying proofs of concept to demonstrate ROI before scaling, managing token procurement and building forecasting/chargeback models to control spend, building dashboards to monitor service level agreements, usage, costs, and error rates, and identifying opportunities for cost savings and performance tuning.
Product Manager, Ghostwriter
As Product Manager for Ghostwriter, you will define how humans interact with software in the agent era, owning the end-to-end product experience from prompt to agent to outcome and helping scale Sierra to thousands of customers. You will define how AI augments agent development by shaping the workflows by which CX teams and developers draft journeys, run simulations, analyze conversations, and improve agents using natural language. You will balance autonomy and control by designing the appropriate human-in-the-loop patterns such as approval flows, change review, and workspace isolation to ensure customer trust in Ghostwriter's changes to their agents. You will partner closely with AI/ML and platform engineering teams to collaborate on model selection, harness engineering, execution architecture, and evaluation/testing infrastructure. Additionally, you will act as the voice of the agent builder by deeply understanding the pain points of CX managers, agent developers, and technical teams configuring journeys, integrations, and simulations.
Product Manager (Agents)
Lead the Lovable agent end-to-end by owning quality, roadmap, and feedback loops to improve it. Represent the user by synthesizing findings on agent performance and behavior and communicating these to the team clearly. Run discovery processes including user interviews, competitive research, evaluation analysis, prompt experimentation, and messaging for new agent capabilities. Own the quality bar for agent outputs by driving evaluation infrastructure, monitoring regressions, and ensuring continuous improvement with every release. Scope features carefully to deliver the right functionality, validate through user feedback and metrics, and eliminate non-effective parts. Enable sales, support, and marketing teams with the necessary context to communicate new agent capabilities effectively. Initial projects include rebuilding the agent evaluation framework to catch regressions before release, discovering gaps in agent reliability and trust, and defining and shipping the first iteration of improved agent error recovery and communication.
Senior Product Manager – Agentic AI Systems
Define and execute product initiatives for agentic AI systems focusing on measurable customer and business outcomes. Own significant parts of the agentic system lifecycle, including orchestration, decisioning, evaluation, and iteration. Contribute to building a repeatable framework for launching, evaluating, and improving agentic capabilities across customers. Help define how agentic systems are measured and improved in production, balancing autonomy with safety and reliability. Partner closely with Engineering, Applied AI/ML, Design, and Solutions teams to ship production-ready systems. Work directly with customers to understand workflows, requirements, and success criteria. Drive customer-informed prioritization by staying close to live deployments and real usage patterns. Support best practices for agent evaluation, iteration, and safe rollout. Represent the product in customer conversations, demos, and feedback sessions.
AI Product Manager
The AI Product Manager is responsible for rapidly prototyping and shipping new security features using AI coding tools and modern development workflows, leveraging internal data, threat intelligence, and market signals to identify high-impact opportunities and validate product direction. They build and scale features across cloud platforms, APIs, and security infrastructure using GenAI while maintaining high standards for reliability and security. They own product definition end-to-end by translating ambiguous problems into clear requirements and shipped solutions, balancing trade-offs across security effectiveness, performance, usability, and operational complexity. The role involves partnering cross-functionally with engineering, security researchers, and business stakeholders to deliver impactful outcomes, identifying opportunities to automate workflows using agentic AI systems and internal tooling, and contributing to the evolution of next-generation AI-driven cybersecurity capabilities including detection, response, and analysis systems.
Product Manager, Agent Harness & Modelling
Define and own the roadmap for North's agent harness, including the agent loop, context engineering layer, tool orchestration, sandbox execution, and sub-agent delegation. Serve as the primary interface between North engineering and Cohere's Modeling team, ensuring new harness capabilities are validated before being built and that neither team limits future possibilities. Own North's agentic evaluation framework, ensuring evaluations are compatible with both the North harness and Modeling's training infrastructure, serving as a reliable bridge between product and research. Engage enterprise customers to identify real-world agentic failures and translate findings into product and model requirements. Stay current with the open-source and commercial agent ecosystem and drive adoption decisions that align North's architecture with emerging standards.
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